Hybrid exact-approximate design approach for sparse functional data

Ming Hung Kao; Ping Han Huang

doi:10.1016/j.csda.2023.107850

Hybrid exact-approximate design approach for sparse functional data

Research output: Contribution to journal › Article › peer-review

Abstract

Optimal designs for sparse functional data under the functional empirical component (FEC) settings are studied. This design issue has some unique features, making it different from classical design problems. To efficiently obtain optimal exact and approximate designs, new computational methods and useful theoretical results are developed, and a hybrid exact-approximate design approach is proposed. The proposed methods are demonstrated to be efficient via simulation studies and a real example.

Original language	English (US)
Article number	107850
Journal	Computational Statistics and Data Analysis
Volume	190
DOIs	https://doi.org/10.1016/j.csda.2023.107850
State	Published - Feb 2024
Externally published	Yes

Keywords

Design efficiency
Longitudinal data
Mixed model equations
Principal components
Random effects

ASJC Scopus subject areas

Statistics and Probability
Computational Mathematics
Computational Theory and Mathematics
Applied Mathematics

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10.1016/j.csda.2023.107850

Cite this

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abstract = "Optimal designs for sparse functional data under the functional empirical component (FEC) settings are studied. This design issue has some unique features, making it different from classical design problems. To efficiently obtain optimal exact and approximate designs, new computational methods and useful theoretical results are developed, and a hybrid exact-approximate design approach is proposed. The proposed methods are demonstrated to be efficient via simulation studies and a real example.",

keywords = "Design efficiency, Longitudinal data, Mixed model equations, Principal components, Random effects",

author = "Kao, {Ming Hung} and Huang, {Ping Han}",

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AB - Optimal designs for sparse functional data under the functional empirical component (FEC) settings are studied. This design issue has some unique features, making it different from classical design problems. To efficiently obtain optimal exact and approximate designs, new computational methods and useful theoretical results are developed, and a hybrid exact-approximate design approach is proposed. The proposed methods are demonstrated to be efficient via simulation studies and a real example.

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KW - Longitudinal data

KW - Mixed model equations

KW - Principal components

KW - Random effects

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Hybrid exact-approximate design approach for sparse functional data

Abstract

Keywords

ASJC Scopus subject areas

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